Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of transfer learning which uses a single output neural network and additional contextual inputs for learning multiple tasks. Motivated by problems with the application of standard MTL networks to machine lifelong learning, csMTL was developed and found to improve predictive performance for tasks from impoverished training sets. The csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination of other ML models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning algorithms.Recent work on a modified neural network classifier for the WEKA data mining suite that accepts csMTL encoded data will be presented. And a new csMTL application that transfers knowledge between image transformation tasks will be demonstrated using some familiar facial images.

ABOUT THE SPEAKER:

Dr. Danny Silver is a Professor in and the Director of the Jodrey School of Computer Science, Acadia University, Wolfville, Nova Scotia. His duties include teaching courses in artificial intelligence, software engineering and most recently Green IT. He completed a Ph.D. in Computer Science from the University of Western Ontario, London, Ontario, after spending 15 years in the software industry. His research focuses on advanced methods of machine learning and their application in data mining, user modeling and intelligent agents. Dr. Silver has been funded since 2000 by NSERC for research into advanced machine learning methods. He has published papers and conference proceedings in the areas of artificial intelligence, business informatics, environmental informatics, and human-computer interaction. From 2007-09 he was the President of the Canadian AI Association (CAIAC), and is currently the Past-President. Prior to his appointment at Acadia he held a position as Research Associate to the Killam Chair in Business Informatics in the Faculty of Management, Dalhousie University. Since January, 1993, he has operated a consulting business (CogNova Technologies) that offers services in the areas of knowledge discovery and data mining to companies such as London Life, 3M Canada, Bell Aliant and the Nova Scotia Department of Health.